Implementation of a Butterworth digital filter using Python libraries

Abstract

Formulation of the problem. In digital signal processing, noise refers to unwanted or random elements of a signal that can distort or corrupt its information content. Such noise can arise from various sources, including electromagnetic interference, thermal noise, quantization errors, and degradation of the transmission channel. It is necessary to deal with noise in a digital signal, as it can degrade the quality and reliability of the signal, lead to errors, distortions and loss of information. Moreover, noise can limit the performance of many digital signal processing systems, such as communications and audio processing systems. Therefore, various methods and algorithms have been developed to reduce noise and improve the quality of digital signals, the main of which is filtering. Target. Increasing the efficiency of digital signal processing systems using filters that allow you to programmatically change the signal filtering parameters. Development of a signal processing system in the Python programming language that implements the Butterworth digital filter algorithm. Results. The architectures of filters with infinite impulse response (IIR filters), features of the Butterworth filter are considered. A software model of a Butterworth digital filter in the Python programming language has been developed, and its verification has been carried out in relation to an audio file. The work of the model is based on the use of Python libraries such as Matplotlib, SciPy and Librosa, which allow you to implement the required algorithm and get the visualization of the signal. Practical significance. The results obtained can later be used as a module of a programmable universal digital signal processing system.